Abstract
In recent years it has been pointed out that, in a number of applications involving classification, the final goal is not determining which class (or classes) individual unlabelled data items belong to, but determining the prevalence (or “relative frequency”) of each class in the unlabelled data. The latter task has come to be known as quantification [1, 3, 5-10, 15, 18, 19].
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References
Baccianella, S., Esuli, A., Sebastiani, F.: Variable-constraint classification and quantification of radiology reports under the ACR Index. Expert Systems and Applications 40(9), 3441–3449 (2013)
Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Quantification via probability estimators. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2010), pp. 737–742 (2010)
Bella, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Aggregative quantification for regression. Data Mining and Knowledge Discovery 28(2), 475–518 (2014)
Esuli, A., Sebastiani, F.: Machines that learn how to code open-ended survey data. International Journal of Market Research 52(6), 775–800 (2010)
Esuli, A., Sebastiani, F.: Sentiment quantification. IEEE Intelligent Systems 25(4), 72–75 (2010)
Esuli, A., Sebastiani, F.: Optimizing text quantifiers for multivariate loss functions. Technical Report 2013-TR-005, Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, IT (2013)
Forman, G.: Counting positives accurately despite inaccurate classification. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 564–575. Springer, Heidelberg (2005)
Forman, G.: Quantifying trends accurately despite classifier error and class imbalance. In: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, US, pp. 157–166 (2006)
Forman, G.: Quantifying counts and costs via classification. Data Mining and Knowledge Discovery 17(2), 164–206 (2008)
Forman, G., Kirshenbaum, E., Suermondt, J.: Pragmatic text mining: Minimizing human effort to quantify many issues in call logs. In: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, US, pp. 852–861 (2006)
Gamon, M.: Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), Geneva, CH, pp. 841–847 (2004)
Giorgetti, D., Sebastiani, F.: Automating survey coding by multiclass text categorization techniques. Journal of the American Society for Information Science and Technology 54(14), 1269–1277 (2003)
Hopkins, D.J., King, G.: A method of automated nonparametric content analysis for social science. American Journal of Political Science 54(1), 229–247 (2010)
Kelly, M.G., Hand, D.J., Adams, N.M.: The impact of changing populations on classifier performance. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1999), San Diego, US, pp. 367–371 (1999)
Milli, L., Monreale, A., Rossetti, G., Giannotti, F., Pedreschi, D., Sebastiani, F.: Quantification trees. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM 2013), Dallas, US, pp. 528–536 (2013)
Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset shift in machine learning. The MIT Press, Cambridge (2009)
Sammut, C., Harries, M.: Concept drift. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 202–205. Springer, Heidelberg (2011)
Tang, L., Gao, H., Liu, H.: Network quantification despite biased labels. In: Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG 2010), Washington, US, pp. 147–154 (2010)
Xue, J.C., Weiss, G.M.: Quantification and semi-supervised classification methods for handling changes in class distribution. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD 2009), Paris, FR, pp. 897–906 (2009)
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Sebastiani, F. (2014). Text Quantification. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_104
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DOI: https://doi.org/10.1007/978-3-319-06028-6_104
Publisher Name: Springer, Cham
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